Computer Vision Methods For Coral Reef Assessment

Background

The world’s natural systems are at a tipping point and many
biologists believe that we are facing a massive ecological extinction
event as a result of anthropogenic impacts on the planet
(Pimm et al. 1995, Wilson 2002). Globally coral reefs face an
unprecedented convergence of stressors including overfishing,
pollution, diseases, destructive fishing practices, sedimentation,
and coastal development that have led to led to massive global declines
(Kline et al 2005, Jackson et al 2001, Hughes et al., 2003 ;Pandolfi
et al 2005; Hoegh-Guldberg, 2007).

Approach

Traditional coral reef monitoring has required trained experts scuba
diving on reefs to specify coverage and health of the key ecological
groups (corals, algae, other invertebrates, fish, bare substrate, etc.).
Recently, photographs or videos now routinely complement such surveys
by experts but there is lacking a rapid, objective, quantitative, and
automated classification of digital imagery. New technologies are
needed to improve the efficiency and objectivity of surveys to assess
the health of global coral reef communities on appropriate temporal
and spatial scales. Analyses of these growing digital image archives
remain extremely constrained by the extensive effort required by coral
experts.

A convergence of several rapidly advancing technologies, including
digital imaging, computational mass storage and processing speed,
integrated with computer vision image analysis, now makes
it feasible to acquire, archive, and digitally classify important
aspects of coral reef community ecology and physiology.
Computer vision technology has considerable potential to
address these problems for coral reef ecosystems, but additional
innovations by an interdisciplinary team are required to
overcome challenges before a robust, automated cyber-enabled
image analysis system can be confidently used for objective
coral reef monitoring.

Main Challenges

Developing computer vision methods to maximize the information that
can be obtained from underwater digital images of coral reefs. The computer
vision team will develop original segmentation and classification
algorithms using our unprecedentedly large, unique data set of controlled,
high resolution digital images of hundreds of tagged coral reef targets
acquired over a 6 year period in Panama, and a 4 year period on the Great
Barrier Reef. Portions of the datasets will first be classified by the
coral ecology experts and then the classified images will be used to train
the computer vision system, resulting in a state-of-the-art computational
method for analyzing reef ecology.

Development of an advanced underwater imaging system with
co-registered RGB and fluorescence image planes to provide additional
optical and physiological information that will increase the accuracy
and speed of the classification process. Multispectral images,
co-registered fluorescence images, turbidity data, ranging data,
and other optical water column corrections and physiological
measurements will be incorporated in order to maximize the
discriminatory ability of the system. This system will be deployed as
part of the continuing time-series in Panama and the Great Barrier
Reef.